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Pathological OCT Retinal Layer Segmentation Using Branch Residual U-Shape Networks

  • Stefanos Apostolopoulos
  • Sandro De Zanet
  • Carlos Ciller
  • Sebastian Wolf
  • Raphael Sznitman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

The automatic segmentation of retinal layer structures enables clinically-relevant quantification and monitoring of eye disorders over time in OCT imaging. Eyes with late-stage diseases are particularly challenging to segment, as their shape is highly warped due to pathological biomarkers. In this context, we propose a novel fully-Convolutional Neural Network (CNN) architecture which combines dilated residual blocks in an asymmetric U-shape configuration, and can segment multiple layers of highly pathological eyes in one shot. We validate our approach on a dataset of late-stage AMD patients and demonstrate lower computational costs and higher performance compared to other state-of-the-art methods.

Supplementary material

455908_1_En_34_MOESM1_ESM.pdf (19.1 mb)
Supplementary material 1 (pdf 19529 KB)

References

  1. 1.
    Huang, D., Swanson, E.A., Lin, C.P., Schuman, J.S., Stinson, W.G., Chang, W., Hee, M.R., Flotte, T., Gregory, K., Puliafito, C.A., Fujimoto, J.G.: Optical coherence tomography HHS public access. Science 254(5035), 1178–1181 (1991)CrossRefGoogle Scholar
  2. 2.
    Abramoff, M., Garvin, M., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)CrossRefGoogle Scholar
  3. 3.
    Morgan, J.I.W.: The fundus photo has met its match: optical coherence tomography and adaptive optics ophthalmoscopy are here to stay. Ophthalmic Physiol. Opt. 36(3), 218–239 (2016)Google Scholar
  4. 4.
    Mayer, M.A., Hornegger, J., Mardin, C.Y., Tornow, R.P.: Retinal nerve fiber layer segmentation on FD-OCT scans of normal subjects and glaucoma patients. Biomed. Opt. Express 1(5), 1358–1383 (2010)Google Scholar
  5. 5.
    Dufour, P.A., Ceklic, L., Abdillahi, H., Schroder, S., De Zanet, S., Wolf-Schnurrbusch, U., Kowal, J.: Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints. IEEE Trans. Med. Imaging 32(3), 531–543 (2013)CrossRefGoogle Scholar
  6. 6.
    Chen, X., Niemeijer, M., Zhang, L., Lee, K., Abramoff, M.D., Sonka, M.: Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut. IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012)CrossRefGoogle Scholar
  7. 7.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_28 CrossRefGoogle Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. 7(3), 171–180 (2015). Arxiv.org
  9. 9.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_49 CrossRefGoogle Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016Google Scholar
  11. 11.
    Wu, Z., Shen, C., van den Hengel, A.: Wider or deeper: revisiting the ResNet model for visual recognition (2016)Google Scholar
  12. 12.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift, pp. 1–11 (2015)Google Scholar
  13. 13.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015Google Scholar
  14. 14.
    Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, Inception-ResNet and the impact of residual connections on learning, p. 12 (2016)Google Scholar
  15. 15.
    Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks, pp. 1–12 (2016)Google Scholar
  16. 16.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)Google Scholar
  17. 17.
    Butt, M., Maragos, P.: Optimum design of Chamfer distance transforms. IEEE Trans. Image Process. 7(10), 1477–1484 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Stefanos Apostolopoulos
    • 1
  • Sandro De Zanet
    • 2
  • Carlos Ciller
    • 2
  • Sebastian Wolf
    • 3
  • Raphael Sznitman
    • 1
  1. 1.University of BernBernSwitzerland
  2. 2.RetinAI Medical GmbHBernSwitzerland
  3. 3.University Hospital of BernBernSwitzerland

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